{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (40, 20),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "## 自定义logistic分类绘图方法\n",
    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, c=cmap(idx),marker=markers[idx], label=cl)\n",
    "    # highlight test samples\n",
    "    if test_idx:\n",
    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "        plt.scatter(X_test[:, 0], X_test[:, 1], c='lightgreen', alpha=1.0, linewidth=1, marker='o', s=55, label='test set')\n",
    "\n",
    "df = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df[\"Plasma_glucose_concentration\"],bins=30,kde=True)\n",
    "plt.xlabel(\"Plasma_glucose_concentration\",fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=df, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a2a33cda0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a261b40f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a26191080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a261aa710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a29c8bb38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a29c8beb8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_features = ['blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI','Diabetes_pedigree_function','Age']\n",
    "df[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a247c0c50>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = df.corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n",
    "\n",
    "#从相关度可了解到发病与【口服葡萄糖耐量试验】／BMI ／年龄／怀孕次数 等相关度较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'pregnants distribution of Target')]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=df[['Triceps_skin_fold_thickness', 'serum_insulin']], x=\"Triceps_skin_fold_thickness\",y=\"serum_insulin\")\n",
    "ax.set(title=\"Age distribution of Target\")\n",
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=df[['Triceps_skin_fold_thickness', 'BMI']], x=\"Triceps_skin_fold_thickness\",y=\"BMI\")\n",
    "ax.set(title=\"pregnants distribution of Target\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.09x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(\"BMI\",\"Plasma_glucose_concentration\",data=df,hue=\"Target\",fit_reg=False);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a220b3f98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数\n",
    "#在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "#标记完缺失值之后，可以利用isnull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量了。\n",
    "\n",
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "df[NaN_col_names] = df[NaN_col_names].replace(0, np.NaN)\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a29f49b70>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = df.corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n",
    "#将0变为NaN后，相关度有了明显的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing  \\\n",
       "0                         35.0                                    0   \n",
       "1                         29.0                                    0   \n",
       "2                          NaN                                    1   \n",
       "3                         23.0                                    0   \n",
       "4                         35.0                                    0   \n",
       "5                          NaN                                    1   \n",
       "6                         32.0                                    0   \n",
       "7                          NaN                                    1   \n",
       "8                         45.0                                    0   \n",
       "9                          NaN                                    1   \n",
       "\n",
       "   serum_insulin  serum_insulin_Missing  \n",
       "0            NaN                      1  \n",
       "1            NaN                      1  \n",
       "2            NaN                      1  \n",
       "3           94.0                      0  \n",
       "4          168.0                      0  \n",
       "5            NaN                      1  \n",
       "6           88.0                      0  \n",
       "7            NaN                      1  \n",
       "8          543.0                      0  \n",
       "9            NaN                      1  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "df['Triceps_skin_fold_thickness_Missing'] = df['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "df['serum_insulin_Missing'] = df['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "df[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing','serum_insulin','serum_insulin_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2a3ffcf8>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23d76b00>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征是否缺失好像和目标也没什么关系.感觉特征缺失是随机的，将这新增的特征删除，老实用中值填补算了。\n",
    "df.drop([\"Triceps_skin_fold_thickness_Missing\", \"serum_insulin_Missing\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = df.median() \n",
    "df = df.fillna(medians)\n",
    "\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.848324</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.204013</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.123396</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.530902</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.684422</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>1.943724</td>\n",
       "      <td>-0.263941</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-1.103255</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.998208</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>0.123302</td>\n",
       "      <td>-0.494043</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504055</td>\n",
       "      <td>-1.504687</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>0.765836</td>\n",
       "      <td>1.409746</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.848324        0.149641   \n",
       "1  -0.844885                     -1.123396       -0.160546   \n",
       "2   1.233880                      1.943724       -0.263941   \n",
       "3  -0.844885                     -0.998208       -0.160546   \n",
       "4  -1.141852                      0.504055       -1.504687   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.907270      -0.692891  0.204013   \n",
       "1                     0.530902      -0.692891 -0.684422   \n",
       "2                    -1.288212      -0.692891 -1.103255   \n",
       "3                     0.154533       0.123302 -0.494043   \n",
       "4                     0.907270       0.765836  1.409746   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据标准化\n",
    "\n",
    "y_train = df['Target']   \n",
    "X_train_no_target = df.drop([\"Target\"], axis=1)\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train_no_target.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "# 进行标准化处理\n",
    "X_train_std_no_target = ss_X.fit_transform(X_train_no_target)\n",
    "#存为csv格式\n",
    "X_train_std_no_target = pd.DataFrame(columns = feat_names, data = X_train_std_no_target)\n",
    "\n",
    "train = pd.concat([X_train_std_no_target, y_train], axis = 1)\n",
    "\n",
    "train.to_csv('My_FE_pima-indians-diabetes.csv',index = False,header=True)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02 0.98]\n",
      " [0.8  0.2 ]\n",
      " [0.89 0.11]\n",
      " ...\n",
      " [0.16 0.84]\n",
      " [0.85 0.15]\n",
      " [0.97 0.03]]\n",
      "LogisticRegression算法log似然损失： 0.4925612164817681\n",
      "LogisticRegression算法正确率accuracy： 0.7973856209150327\n",
      "#################################\n",
      "LogisticRegression准确率 0.7406006674082314 [0.61 0.77 0.71 0.71 0.9 ]\n",
      "LogisticRegression精确率： 0.6323626373626373 [0.38 0.62 0.57 0.6  1.  ]\n",
      "LogisticRegression召回率： 0.49333333333333335 [0.3  0.8  0.4  0.3  0.67]\n",
      "LogisticRegressionF1指标： 0.5399147485080988 [0.33 0.7  0.47 0.4  0.8 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a26949518>"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#训练数据和测试数据分割\n",
    "X_train_std,X_test_std,y_train,y_test = train_test_split(X_train_std_no_target, y_train, test_size=0.8, random_state=0)\n",
    "\n",
    "###  先看下普通LogisticRegression的效果  ###\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train_std, y_train)\n",
    "\n",
    "logreg_y_predict = logreg.predict(X_test_std) # 返回X对应的预测值(类别标签)\n",
    "logreg_y_proba_predict = logreg.predict_proba(X_test_std) \n",
    "\n",
    "print(logreg_y_proba_predict)\n",
    "\n",
    "logreg_log_loss = metrics.log_loss(y_test,logreg_y_proba_predict);\n",
    "print (\"LogisticRegression算法log似然损失：\", logreg_log_loss)\n",
    "\n",
    "print (\"LogisticRegression算法正确率accuracy：\", logreg.score(X_train_std, y_train))\n",
    "\n",
    "print('#################################')\n",
    "scores=cross_val_score(logreg,X_train_std,y_train,cv=5)\n",
    "print ('LogisticRegression准确率',np.mean(scores),scores)\n",
    "precisions = cross_val_score(logreg, X_train_std, y_train, cv=5, scoring='precision')\n",
    "print ('LogisticRegression精确率：', np.mean(precisions), precisions)\n",
    "recalls = cross_val_score(logreg, X_train_std, y_train, cv=5, scoring='recall')\n",
    "print ('LogisticRegression召回率：', np.mean(recalls), recalls)\n",
    "fls = cross_val_score(logreg, X_train_std, y_train, cv=5, scoring='f1')\n",
    "print ('LogisticRegressionF1指标：', np.mean(fls), fls)\n",
    "plt.scatter(recalls, precisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 2)\n",
      "(768,)\n"
     ]
    }
   ],
   "source": [
    "# 做图,2D平面，因此只选择两个特征。\n",
    "# 类似可参考https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py\n",
    "# more：  https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "\n",
    "X_train_std_2feature = X_train_std[['BMI','Plasma_glucose_concentration']]   #选取2个特征\n",
    "#X_train_std_2feature.head()\n",
    "X_test_std_2feature = X_test_std[['BMI','Plasma_glucose_concentration']]\n",
    "\n",
    "\n",
    "logreg1 = LogisticRegression()\n",
    "logreg1.fit(X_train_std_2feature, y_train)\n",
    "logreg1_y_proba_predict = logreg1.predict_proba(X_test_std_2feature) \n",
    "X_combined_std = np.vstack((X_train_std_2feature, X_test_std_2feature))\n",
    "y_combined = np.hstack((y_train, y_test))\n",
    "print(X_combined_std.shape)\n",
    "print(y_combined.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "plot_decision_regions(X_combined_std, y_combined, classifier=logreg1)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression算法AUC值： 0.8245814307458141\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "# 这里绘制LogisticRegression的ROC图和计算AUC值\n",
    "\n",
    "y_test_hot = label_binarize(y_test,classes=(0,1)) ## 将数据转换为矩阵形式\n",
    "logreg_y_score = logreg.decision_function(X_test_std) \n",
    "logreg_fpr, logreg_tpr, logreg_threasholds = metrics.roc_curve(y_test_hot.ravel(),logreg_y_score.ravel())\n",
    "## 计算auc的值\n",
    "logreg_auc = metrics.auc(logreg_fpr, logreg_tpr)\n",
    "\n",
    "print (\"LogisticRegression算法AUC值：\", logreg_auc)\n",
    "\n",
    "x_test_len = range(len(X_test_std))\n",
    "## 画图1：ROC曲线画图\n",
    "plt.figure(figsize=(8, 6), facecolor='w')\n",
    "plt.plot(lr_fpr,lr_tpr,c='r',lw=2,label=u'logreg,AUC=%.3f' % logreg_auc)\n",
    "\n",
    "plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')\n",
    "plt.xlim(-0.01, 1.02) #设置X轴的最大和最小值\n",
    "plt.ylim(-0.01, 1.02) #设置y轴的最大和最小值\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "\n",
    "plt.xlabel('False Positive Rate(FPR)', fontsize=16)\n",
    "plt.ylabel('True Positive Rate(TPR)', fontsize=16)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "plt.title(u'logreg ROC/AUC', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.04 0.96]\n",
      " [0.8  0.2 ]\n",
      " [0.88 0.12]\n",
      " ...\n",
      " [0.2  0.8 ]\n",
      " [0.84 0.16]\n",
      " [0.96 0.04]]\n",
      "LogisticRegressionCV算法log似然损失： 0.49357021134585616\n",
      "LogisticRegressionCV算法正确率accuracy： 0.7973856209150327\n"
     ]
    }
   ],
   "source": [
    "#然后使用 LogisticRegressionCV 做比较\n",
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "\n",
    "#scoring默认返回准确率，cv=5为5折交叉验证\n",
    "#solver：newton-cg’牛顿法, ‘lbfgs’有限内存的拟牛顿法, ‘liblinear’为百万级别的数据和特征实现的线性分类器, \n",
    "#‘sag’随机平均梯度算法, ‘saga’快速梯度下降法。缺省:liblinear\n",
    "logregCV = LogisticRegressionCV(Cs=10,cv=5,solver='lbfgs', penalty='l2',intercept_scaling=1.0, multi_class='ovr')\n",
    "logregCV.fit(X_train_std, y_train)\n",
    "\n",
    "## 模型预测\n",
    "# 返回X对应的预测值(类别标签)\n",
    "lr_y_predict = logregCV.predict(X_test_std)\n",
    "#print(lr_y_predict)\n",
    "\n",
    "#返回X对应的预测值(每个类别对应的概率的log值)\n",
    "lr_y_proba_log_predict = logregCV.predict_log_proba(X_test_std)\n",
    "#print(lr_y_proba_log_predict)\n",
    "\n",
    "#返回X对应的预测值(每个类别对应的概率)\n",
    "lr_y_proba_predict = logregCV.predict_proba(X_test_std)\n",
    "print(lr_y_proba_predict)\n",
    "\n",
    "\n",
    "#若p=0.5，logloss=0.693   so只要loglss是在0.693以上，就说明模型是失败的\n",
    "#若p=0.6，logloss=0.673 ; p=0.7，logloss=0.611 ; p=0.8，logloss=0.500 ; p=0.9，logloss=0.325\n",
    "lr_log_loss = metrics.log_loss(y_test,lr_y_proba_predict);\n",
    "print (\"LogisticRegressionCV算法log似然损失：\", lr_log_loss)\n",
    "print (\"LogisticRegressionCV算法正确率accuracy：\", logregCV.score(X_train, y_train))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegressionCV算法AUC值： 0.8255961440892947\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#计算并画出LogisticRegressionCV ROC\n",
    "\n",
    "y_test_hot = label_binarize(y_test,classes=(0,1)) ## 将数据转换为矩阵形式\n",
    "lr_y_score = logregCV.decision_function(X_test_std) ## 返回X对应的预测值(置信值，样本到决策超平面的带符号距离)\n",
    "lr_fpr, lr_tpr, lr_threasholds = metrics.roc_curve(y_test_hot.ravel(),lr_y_score.ravel())\n",
    "## 计算auc的值\n",
    "lr_auc = metrics.auc(lr_fpr, lr_tpr)\n",
    "\n",
    "print (\"LogisticRegressionCV算法AUC值：\", lr_auc)   #略好于LogisticRegression\n",
    "\n",
    "x_test_len = range(len(X_test_std))\n",
    "## 画图1：ROC曲线画图\n",
    "plt.figure(figsize=(8, 6), facecolor='w')\n",
    "plt.plot(lr_fpr,lr_tpr,c='r',lw=2,label=u'logregCV,AUC=%.3f' % lr_auc)\n",
    "\n",
    "plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')\n",
    "plt.xlim(-0.01, 1.02) #设置X轴的最大和最小值\n",
    "plt.ylim(-0.01, 1.02) #设置y轴的最大和最小值\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "\n",
    "plt.xlabel('False Positive Rate(FPR)', fontsize=16)\n",
    "plt.ylabel('True Positive Rate(TPR)', fontsize=16)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "plt.title(u'logregCV ROC/AUC', fontsize=18)\n",
    "plt.show()\n",
    "\n",
    "#整体上CV较LogisticRegression，曲线更平滑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[359  37]\n",
      " [109 110]]\n",
      "Normalized confusion matrix\n",
      "[[0.91 0.09]\n",
      " [0.5  0.5 ]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 混淆矩阵\n",
    "\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "class_names = ['Target-1','Target-0']\n",
    "\n",
    "# 看看LogisticRegressionCV 的表现\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, lr_y_predict)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LRCV              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.91      0.83       396\n",
      "          1       0.75      0.50      0.60       219\n",
      "\n",
      "avg / total       0.76      0.76      0.75       615\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('LRCV',classification_report(y_test,lr_y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.78      0.90      0.84       396\n",
      "          1       0.75      0.53      0.62       219\n",
      "\n",
      "avg / total       0.77      0.77      0.76       615\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ('LR',classification_report(y_test,logreg_y_predict))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交的晚了，不足之处多多指正，多谢啦～～～ "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
